Title :
Pruning a classifier based on a self-organizing map using Boolean function formalization
Author :
Lobo, Victor Jose ; Swiniarski, Roman ; Moura-Pires, Fernando
Author_Institution :
Escola Naval, Portugeses Naval Acad., Almada, Portugal
Abstract :
An algorithm is presented to minimize the number of neurons needed for a classifier based on Kohonens self-organizing maps (SOM), or on any other “code-book type” (or “prototype based”) classifier such as Kohonens linear vector quantization (LVQ), K-means or nearest neighbor. The neuron minimization problem is formalized as a problem of simplification of Boolean functions, and a geometric interpretation of this simplification is provided. A step by step example with an illustrative classification problem is given
Keywords :
Boolean functions; covariance matrices; geometry; learning (artificial intelligence); pattern classification; self-organising feature maps; vector quantisation; Boolean function formalization; K-means classifier; Kohonens linear vector quantization classifier; Kohonens self-organizing maps; code-book type classifier; nearest neighbor classifier; neuron minimization problem; Boolean functions; Covariance matrix; Drives; Gaussian distribution; Nearest neighbor searches; Neural networks; Neurons; Organizing; Prototypes; Vector quantization;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687150
